Objective An instrument for measuring intervention preferences applicable to both patients and policymakers would make it possible to better confront the needs of the supply and demand sides of the health care system. This study aimed to develop a discrete choice experiments (DCE) questionnaire to elicit the preferences of patients and policymakers. The instrument was specifically developed to estimate preferences for new conditions to be added to a screening program for fetal chromosomal anomalies. Methods A DCE development study was conducted. The methods employed included a literature review, a qualitative study (based on individual semi-structured interviews, consultations, and a focus group discussion) with pregnant women and policymakers, and a pilot project with 33 pregnant women to validate the first version of the instrument and test the feasibility of its administration. Results An initial list of 10 attributes was built based on a literature review and the qualitative research components of the study. Five attributes were built based on the responses provided by the participants from both groups. Eight attributes were consensually retained. A pilot project performed on 33 pregnant women led to a final instrument containing seven attributes: ‘conditions to be screened’, ‘test performance’, ‘moment at gestational age to obtain the test result’, ‘degree of test result certainty to the severity of the disability’, ‘test sufficiency’, ‘information provided from test result’, and ‘cost related to the test’. Conclusion It is possible to reach a consensus on the construction of a DCE instrument intended to be administered to pregnant women and policymakers. However, complete validation of the consensual instrument is limited because there are too few voting members of health technology assessment agencies committees to statistically ascertain the relevance of the attributes and their levels.

BackgroundIn an accountable world, being able to take into account the value given by relevant stakeholders to an intervention that could be offered to the population is considered as desirable. DCE is an approach particularly suited for the measurement of such values in the field of prenatal care. Yet, DCE studies in the field of prenatal screening have focused mainly on pregnant women and their care providers but have neglected another key actor, the decision-makers. The objective of the study was to develop a DCE instrument applicable to pregnant women and decision-makers, for the evaluation of new conditions to be added to a screening program for fetal chromosomal anomalies.MethodsAn instrument development study was undertaken. Methods employed included a literature review, a qualitative study performed on pregnant women and decision-makers, and a pilot project to validate the developed instrument and test the feasibility of its administration through an online survey platform. ResultsAn initial list of ten attributes and levels were built from the information provided by the literature review and the qualitative research component of the study. Seven attributes were built based on responses provided by participants from both groups. Two attributes were built from what was said by women only and one from what was said by decision-makers only. Search for consensus through consultations and a focus group discussion led to the retention of eight attributes. A pilot project was then performed with 33 pregnant women. This led to the exclusion of one attribute that showed poor influence on the choice making. The final version of the instrument contains seven attributes.ConclusionThis paper presents the construction of a DCE instrument that can be administered to pregnant women on the demand side, and decision-makers on the supply side. Such an instrument to measure the social desirability of an intervention could be an added value to the decision-making process of Health Technology Assessment agencies.

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Video anomaly detection is one of the hot research topics in computer vision nowadays, as abnormal events contain a high amount of information. Anomalies are one of the main detection targets in surveillance systems, usually needing real-time actions. Regarding the availability of labeled data for training (i.e., there is not enough labeled data for abnormalities), semi-supervised anomaly detection approaches have gained interest recently. This paper introduces the researchers of the field to a new perspective and reviews the recent deep-learning based semi-supervised video anomaly detection approaches, based on a common strategy they use for anomaly detection. Our goal is to help researchers develop more effective video anomaly detection methods. As the selection of a right Deep Neural Network plays an important role for several parts of this task, a quick comparative review on DNNs is prepared first. Unlike previous surveys, DNNs are reviewed from a spatiotemporal feature extraction viewpoint, customized for video anomaly detection. This part of the review can help researchers in this field select suitable networks for different parts of their methods. Moreover, some of the state-of-the-art anomaly detection methods, based on their detection strategy, are critically surveyed. The review provides a novel and deep look at existing methods and results in stating the shortcomings of these approaches, which can be a hint for future works.